Fix AttributeError use_fallback_lbfgs_solve for newton-cholesky when …

…fitting with max_iter=0 (#26653)

Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

doc/whats_new/v1.3.rst

@@ -455,6 +455,11 @@ Changelog
  on linearly separable problems.
  :pr:`25214` by `Tom Dupre la Tour`_.
 
+- |Fix| Fix a crash when calling `fit` on
+ :class:`linear_model.LogisticRegression(solver="newton-cholesky", max_iter=0)`
+ which failed to inspect the state of the model prior to the first parameter update.
+ :pr:`26653` by :user:`Olivier Grisel <ogrisel>`.
+
 - |API| Deprecates `n_iter` in favor of `max_iter` in
  :class:`linear_model.BayesianRidge` and :class:`linear_model.ARDRegression`.
  `n_iter` will be removed in scikit-learn 1.5. This change makes those

sklearn/linear_model/_glm/_newton_solver.py

@@ -375,6 +375,7 @@ def solve(self, X, y, sample_weight):
 
  self.iteration = 1
  self.converged = False
+ self.use_fallback_lbfgs_solve = False
 
  while self.iteration <= self.max_iter and not self.converged:
  if self.verbose:

sklearn/linear_model/tests/test_logistic.py

@@ -2063,3 +2063,29 @@ def test_liblinear_not_stuck():
  with warnings.catch_warnings():
  warnings.simplefilter("error", ConvergenceWarning)
  clf.fit(X_prep, y)
+
+
+@pytest.mark.parametrize("solver", SOLVERS)
+def test_zero_max_iter(solver):
+ # Make sure we can inspect the state of LogisticRegression right after
+ # initialization (before the first weight update).
+ X, y = load_iris(return_X_y=True)
+ y = y == 2
+ with ignore_warnings(category=ConvergenceWarning):
+ clf = LogisticRegression(solver=solver, max_iter=0).fit(X, y)
+ if solver not in ["saga", "sag"]:
+ # XXX: sag and saga have n_iter_ = [1]...
+ assert clf.n_iter_ == 0
+
+ if solver != "lbfgs":
+ # XXX: lbfgs has already started to update the coefficients...
+ assert_allclose(clf.coef_, np.zeros_like(clf.coef_))
+ assert_allclose(
+ clf.decision_function(X),
+ np.full(shape=X.shape[0], fill_value=clf.intercept_),
+ )
+ assert_allclose(
+ clf.predict_proba(X),
+ np.full(shape=(X.shape[0], 2), fill_value=0.5),
+ )
+ assert clf.score(X, y) < 0.7